Unlock the Power of Machine Learning: Learn the Techniques that are Revolutionizing Industry!
Machine Learning is transforming the world as we know it. From improving healthcare to predicting market trends, this innovative technology…
The Future Is Here: The Latest News And Developments In The World Of AI!
Artificial intelligence (AI) is rapidly evolving, and it is becoming an integral part of our daily lives. From business to…
The World Won’t Wait: Evaluating and Teaching Agents Under a Live Clock
arXiv:2610.20665v1 Announce Type: new Abstract: Large language model (LLM) agents can solve complex interactive tasks, yet reasoning and tool calls…
Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling
arXiv:2610.20497v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) trains language models from checked task outcomes, but assigning…
Retrieved but Not Read: Understanding Why Search-Augmented LLMs Fail to Use Available Evidence
arXiv:2610.21731v1 Announce Type: cross Abstract: Web search allows LLMs to access up-to-date information, but it remains unclear why they sometimes…
FIRM: Fisher-Informed Retention Merging for Federated LLM Unlearning
arXiv:2610.21720v1 Announce Type: cross Abstract: Federated LLM unlearning addresses targeted knowledge removal when forget and retain data remain distributed across…
Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection
arXiv:2610.20243v2 Announce Type: replace-cross Abstract: Anomaly detection is a fundamental task for time-series analytics, with important implications for the downstream…
Rethinking Quality in Large Language Model Routing: Security-Conditioned Model-and-Budget Selection
arXiv:2610.18566v1 Announce Type: new Abstract: Large language model (LLM) routers reduce serving cost by choosing which model to call and…
Configuring RAG Systems: What Breaks, Where Originates, and How to Fix
arXiv:2610.18567v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a dominant paradigm for improving Large Language Model (LLM)…
Hide and Seek: HIDE Concepts to SEEK explanations for black box models
arXiv:2610.13935v3 Announce Type: replace-cross Abstract: Interpreting machine learning model predictions is a central challenge, particularly for black-box vision models. Concept-based…
How Should a Prompt Optimizer Spend a Tight Budget? BudgetAPO with Noise-Adaptive Evaluation
arXiv:2610.05671v3 Announce Type: replace Abstract: Automatic prompt optimization (APO) has been widely employed to adapt large language models without updating…
Sensitivity or Criterion? Measuring and Moving When Language Models Act
arXiv:2610.15415v1 Announce Type: cross Abstract: Before an agent acts, it must decide whether action is warranted. A single accuracy cannot…